add mcp-playground by ms_agent

apps/mcp-playground/app_mcp_client.py

@@ -0,0 +1,239 @@
+from typing import List, Callable
+from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.language_models import BaseChatModel
+# from langgraph.prebuilt import create_react_agent
+from langchain_mcp_adapters.client import MultiServerMCPClient
+import json
+import os
+import re
+from contextlib import asynccontextmanager
+
+from modelscope_agent.agent import Agent
+
+
+def parse_mcp_config(mcp_config: dict, enabled_mcp_servers: list = None):
+    mcp_servers = {}
+    for server_name, server in mcp_config.get("mcpServers", {}).items():
+        if server["type"] == "stdio" or (enabled_mcp_servers is not None
+                                         and server_name
+                                         not in enabled_mcp_servers):
+            continue
+        new_server = {**server}
+        new_server["transport"] = server["type"]
+        del new_server["type"]
+        if server.get("env"):
+            env = {'PYTHONUNBUFFERED': '1', 'PATH': os.environ.get('PATH', '')}
+            env.update(server["env"])
+            new_server["env"] = env
+        mcp_servers[server_name] = new_server
+    return mcp_servers
+
+
+@asynccontextmanager
+async def get_mcp_client(mcp_servers: dict):
+    async with MultiServerMCPClient(mcp_servers) as client:
+        yield client
+
+
+async def get_mcp_prompts(mcp_config: dict, get_llm: Callable):
+    try:
+        mcp_servers = parse_mcp_config(mcp_config)
+        if len(mcp_servers.keys()) == 0:
+            return {}
+        llm: BaseChatModel = get_llm()
+        async with get_mcp_client(mcp_servers) as client:
+            mcp_tool_descriptions = {}
+            for mcp_name, server_tools in client.server_name_to_tools.items():
+                mcp_tool_descriptions[mcp_name] = {}
+                for tool in server_tools:
+                    mcp_tool_descriptions[mcp_name][
+                        tool.name] = tool.description
+            prompt = f"""Based on the following MCP service tool descriptions, generate 2-4 example user queries for each service:
+
+Input structure explanation:
+- mcp_tool_descriptions is a nested dictionary
+- The first level keys are MCP service names (e.g., "service1", "service2")
+- The second level contains descriptions of tools available within each service
+
+MCP Service Tool Descriptions: {json.dumps(mcp_tool_descriptions)}
+
+Please provide 2-4 natural and specific example queries in Chinese that effectively demonstrate the capabilities of each service.
+
+The response must be in strict JSON format as shown below, with MCP service names as keys:
+```json
+{{
+    "mcp_name1": ["中文示例1", "中文示例2"],
+    "mcp_name2": ["中文示例1", "中文示例2"]
+}}
+```
+
+Ensure:
+1. Each example is specific to the functionality of that particular MCP service
+2. Example queries are in natural Chinese expressions
+3. Strictly use the top-level MCP service names as JSON keys
+4. The returned format must be valid JSON
+5. Each service MUST have exactly 2-4 example queries - not fewer than 2 and not more than 4
+
+Return only the JSON object without any additional explanation or text."""
+            response = await llm.ainvoke(prompt)
+            if hasattr(response, 'content'):
+                content = response.content
+            else:
+                content = str(response)
+            json_match = re.search(r'\{.*\}', content, re.DOTALL)
+            if json_match:
+                json_content = json_match.group(0)
+            else:
+                json_content = content
+            raw_examples = json.loads(json_content)
+
+            for mcp_name in mcp_tool_descriptions.keys():
+                if mcp_name not in raw_examples:
+                    raw_examples[mcp_name] = [
+                        f"请使用 {mcp_name} 服务的功能帮我查询信息或解决问题",
+                    ]
+            return raw_examples
+    except Exception as e:
+        print('Prompt Error:', e)
+        return {
+            mcp_name: [
+                f"请使用 {mcp_name} 服务的功能帮我查询信息或解决问题",
+            ]
+            for mcp_name in mcp_servers.keys()
+        }
+
+
+def convert_mcp_name(tool_name: str, mcp_names: dict):
+    if not tool_name:
+        return tool_name
+    separators = tool_name.split("__TOOL__")
+    if len(separators) >= 2:
+        mcp_name_idx, mcp_tool_name = separators[:2]
+    else:
+        mcp_name_idx = separators[0]
+        mcp_tool_name = None
+    mcp_name = mcp_names.get(mcp_name_idx)
+    if not mcp_tool_name:
+        return mcp_name or mcp_name_idx
+
+    if not mcp_name:
+        return mcp_tool_name
+    return f"[{mcp_name}] {mcp_tool_name}"
+
+
+def generate_with_mcp(messages: List[dict], mcp_config: dict,
+                            enabled_mcp_servers: list, sys_prompt: str,
+                            get_llm: dict, chatbot):
+    mcp_servers = {}
+    mcp_servers["mcpServers"] = parse_mcp_config(mcp_config, enabled_mcp_servers)
+    agent_executor = Agent(
+        mcp=mcp_servers, llm=get_llm, instruction=sys_prompt)
+    response = agent_executor.run("你好")
+    for chunk in response:
+        response += chunk
+        chatbot[-1]["content"] = response
+        yield chatbot
+
+
+#     async with get_mcp_client(mcp_servers) as client:
+#         tools = []
+#         mcp_tools = []
+#         mcp_names = {}
+#         for i, server_name_to_tool in enumerate(
+#                 client.server_name_to_tools.items()):
+#             mcp_name, server_tools = server_name_to_tool
+#             mcp_names[str(i)] = mcp_name
+#             for tool in server_tools:
+#                 new_tool = tool.model_copy()
+#                 # tool match ^[a-zA-Z0-9_-]+$
+#                 new_tool.name = f"{i}__TOOL__{tool.name}"
+#                 mcp_tools.append(new_tool)
+#         tools.extend(mcp_tools)
+#         llm: BaseChatModel = get_llm()
+#         tool_result_instruction = """When a tool returns responses containing URLs or links, please format them appropriately based on their CORRECT content type:
+
+# For example:
+# - Videos should use <video> tags
+# - Audio should use <audio> tags
+# - Images should use ![description](URL) or <img> tags
+# - Documents and web links should use [description](URL) format
+
+# Choose the appropriate display format based on the URL extension or content type information. This will provide the best user experience.
+
+# Remember that properly formatted media will enhance the user experience, especially when content is directly relevant to answering the query.
+# """
+#         attachment_instruction = """
+# The following instructions apply when user messages contain "Attachment links: [...]":
+
+# These links are user-uploaded attachments that contain important information for this conversation. These are temporary, secure links to files the user has specifically provided for analysis.
+
+# IMPORTANT INSTRUCTIONS:
+# 1. These attachments should be your PRIMARY source of information when addressing the user's query.
+# 2. Prioritize analyzing and referencing these documents BEFORE using any other knowledge.
+# 3. If the content in these attachments is relevant to the user's request, base your response primarily on this information.
+# 4. When you reference information from these attachments, clearly indicate which document it comes from.
+# 5. If the attachments don't contain information needed to fully address the query, only then supplement with your general knowledge.
+# 6. These links are temporary and secure, specifically provided for this conversation.
+# 7. IMPORTANT: Do not use the presence of "Attachment links: [...]" as an indicator of the user's preferred language. This is an automatically added system text. Instead, determine the user's language from their actual query text.
+
+# Begin your analysis by examining these attachments first, and structure your thinking to prioritize insights from these documents.
+# """
+#         prompt = ChatPromptTemplate.from_messages([
+#             ("system", tool_result_instruction),
+#             ("system", sys_prompt),
+#             ("system", attachment_instruction),
+#             MessagesPlaceholder(variable_name="messages"),
+#         ])
+
+#         langchain_messages = []
+#         for msg in messages:
+#             if msg["role"] == "user":
+#                 langchain_messages.append(HumanMessage(content=msg["content"]))
+#             elif msg["role"] == "assistant":
+#                 langchain_messages.append(AIMessage(content=msg["content"]))
+
+#         agent_executor = create_react_agent(llm, tools, prompt=prompt)
+#         use_tool = False
+#         async for step in agent_executor.astream(
+#             {"messages": langchain_messages},
+#                 config={"recursion_limit": 50},
+#                 stream_mode=["values", "messages"],
+#         ):
+#             if isinstance(step, tuple):
+#                 if step[0] == "messages":
+#                     message_chunk = step[1][0]
+#                     if hasattr(message_chunk, "content"):
+#                         if isinstance(message_chunk, ToolMessage):
+#                             use_tool = False
+#                             yield {
+#                                 "type":
+#                                 "tool",
+#                                 "name":
+#                                 convert_mcp_name(message_chunk.name,
+#                                                  mcp_names),
+#                                 "content":
+#                                 message_chunk.content
+#                             }
+#                         elif hasattr(message_chunk,
+#                                      'tool_call_chunks') and len(
+#                                          message_chunk.tool_call_chunks) > 0:
+#                             for tool_call_chunk in message_chunk.tool_call_chunks:
+#                                 yield {
+#                                     "type":
+#                                     "tool_call_chunks",
+#                                     "name":
+#                                     convert_mcp_name(tool_call_chunk["name"],
+#                                                      mcp_names),
+#                                     "content":
+#                                     tool_call_chunk["args"],
+#                                     "next_tool":
+#                                     bool(use_tool and tool_call_chunk["name"])
+#                                 }
+#                                 if tool_call_chunk["name"]:
+#                                     use_tool = True
+#                         elif message_chunk.content:
+#                             yield {
+#                                 "type": "content",
+#                                 "content": message_chunk.content
+#                             }

apps/mcp-playground/config.py

@@ -0,0 +1,124 @@
+import json
+import os
+from modelscope_studio.components.pro.chatbot import ChatbotWelcomeConfig, ChatbotUserConfig, ChatbotBotConfig, ChatbotActionConfig
+
+max_mcp_server_count = 10
+
+default_mcp_config = json.dumps({"mcpServers": {
+    "time": {
+      "type": "sse",
+      "url": "https://mcp.api-inference.modelscope.cn/sse/35bd8831457242"
+    }
+}},
+                                indent=4,
+                                ensure_ascii=False)
+
+default_history_config = json.dumps({"history": 
+    [
+        {'role': 'user', 'content': '介绍一下成龙和李连杰', 'sent_from': '', 'send_to': 'all'},
+        {'role': 'assistant', 'content': '他们的职业都是功夫演员', 'sent_from': '', 'send_to': 'all'}
+    ]
+},
+                                indent=4,
+                                ensure_ascii=False)
+
+default_sys_prompt = "You are a helpful assistant."
+
+# for internal
+default_mcp_prompts = {
+    # "arxiv": ["查找最新的5篇关于量子计算的论文并简要总结", "根据当前时间，找到近期关于大模型的论文，得到研究趋势"],
+    "高德地图": ["北京今天天气怎么样", "基于今天的天气，帮我规划一条从北京到杭州的路线"],
+    "time": ["帮我查一下北京时间", "现在是北京时间 2025-04-01 12:00:00，对应的美西时间是多少？"],
+    "fetch":
+    ["从中国新闻网获取最新的新闻", "获取 https://www.example.com 的内容，并提取为Markdown格式"],
+}
+
+# for internal
+default_mcp_servers = [{
+    "name": mcp_name,
+    "enabled": True,
+    "internal": True
+} for mcp_name in default_mcp_prompts.keys()]
+
+bot_avatars = {
+    "Qwen/Qwen2.5-72B-Instruct":
+    os.path.join(os.path.dirname(__file__), "./assets/qwen.png"),
+    "Qwen/QwQ-32B":
+    os.path.join(os.path.dirname(__file__), "./assets/qwen.png"),
+    "LLM-Research/Llama-4-Maverick-17B-128E-Instruct":
+    os.path.join(os.path.dirname(__file__), "./assets/meta.webp"),
+    "deepseek-ai/DeepSeek-V3-0324":
+    os.path.join(os.path.dirname(__file__), "./assets/deepseek.png"),
+}
+
+mcp_prompt_model = "Qwen/Qwen2.5-72B-Instruct"
+
+model_options = [
+    {
+        "label": "Qwen2.5-72B-Instruct",
+        "value": "Qwen/Qwen2.5-72B-Instruct"
+    },
+    {
+        "label": "DeepSeek-V3-0324",
+        "value": "deepseek-ai/DeepSeek-V3-0324",
+    },
+    {
+        "label": "Llama-4-Maverick-17B-128E-Instruct",
+        "value": "LLM-Research/Llama-4-Maverick-17B-128E-Instruct",
+    },
+    {
+        "label": "QwQ-32B",
+        "value": "Qwen/QwQ-32B",
+        "thought": True
+    },
+]
+
+primary_color = "#816DF8"
+
+default_locale = 'zh_CN'
+
+default_theme = {"token": {"colorPrimary": primary_color}}
+
+
+def user_config(disabled_actions=None):
+    return ChatbotUserConfig(actions=[
+        "copy", "edit",
+        ChatbotActionConfig(action='delete',
+                            popconfirm=dict(title="删除消息",
+                                            description="确认删除该消息?",
+                                            okButtonProps=dict(danger=True)))
+    ],
+                             disabled_actions=disabled_actions)
+
+
+def bot_config(disabled_actions=None):
+    return ChatbotBotConfig(actions=[
+        "copy", "edit",
+        ChatbotActionConfig(action="retry",
+                            popconfirm=dict(title="重新生成消息",
+                                            description="重新生成消息会删除所有后续消息。",
+                                            okButtonProps=dict(danger=True))),
+        ChatbotActionConfig(action='delete',
+                            popconfirm=dict(title="删除消息",
+                                            description="确认删除该消息?",
+                                            okButtonProps=dict(danger=True)))
+    ],
+                            disabled_actions=disabled_actions)
+
+
+def welcome_config(prompts: dict, loading=False):
+    return ChatbotWelcomeConfig(
+        icon="./assets/mcp.png",
+        title="MCP 实验场",
+        styles=dict(icon=dict(borderRadius="50%", overflow="hidden")),
+        description="调用 MCP 工具以拓展模型能力",
+        prompts=dict(title="用例生成中..." if loading else None,
+                     wrap=True,
+                     styles=dict(item=dict(flex='1 0 200px')),
+                     items=[{
+                         "label":
+                         mcp_name,
+                         "children": [{
+                             "description": prompt
+                         } for prompt in prompts]
+                     } for mcp_name, prompts in prompts.items()]))

apps/mcp-playground/env.py

@@ -0,0 +1,16 @@
+import os
+import json
+
+is_cn_env = os.getenv('MODELSCOPE_ENVIRONMENT') == 'studio'
+
+api_key = os.getenv('MODELSCOPE_API_KEY')
+
+internal_mcp_config = json.loads(
+    os.getenv("INTERNAL_MCP_CONFIG", '{"mcpServers": {}}'))
+
+# oss
+endpoint = os.getenv("OSS_ENDPOINT")
+
+region = os.getenv("OSS_REGION")
+
+bucket_name = os.getenv("OSS_BUCKET_NAME")

apps/mcp-playground/requirements.txt

@@ -0,0 +1,10 @@
+modelscope_studio
+gradio
+langchain
+langchain-openai
+langchain_core
+langgraph
+langchain_mcp_adapters
+mcp
+exceptiongroup
+oss2